GCR-Net: 3D Graph convolution-based residual network for robust reconstruction in cerenkov luminescence tomography

被引:3
作者
Li, Weitong [1 ,2 ]
Du, Mengfei [1 ,2 ]
Chen, Yi [1 ,2 ]
Wang, Haolin [1 ,2 ]
Su, Linzhi [1 ,2 ]
Yi, Huangjian [1 ]
Zhao, Fengjun [1 ]
Li, Kang [1 ,2 ]
Wang, Lin [3 ]
Cao, Xin [1 ,2 ]
机构
[1] Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Shaanxi, Peoples R China
[2] Natl & Local Joint Engn Res Ctr Cultural Heritage, Xian 710127, Shaanxi, Peoples R China
[3] Xian Univ Technol, Xian 710127, Shaanxi, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Cerenkov luminescence tomography; optical molecular imaging; optical tomography; deep learning; 3D graph convolution; LOCALLY CONNECTED NETWORK; PERMISSIBLE SOURCE REGION; BIOLUMINESCENCE TOMOGRAPHY; IMAGING-SYSTEM; ACCURATE; REGULARIZATION; FRAMEWORK;
D O I
10.1142/S179354582245002X
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.
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页数:11
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